Objective measurement of physical activity in a random sample of Saint-Petersburg inhabitants

Background: World Health Organization (WHO) experts listed physical inactivity in leading risk factors for global mortality. Current research shows that only objective measurement of physical activity may provide accurate information on this parameter. The aim of our study was to assess the 7-day physical activity monitoring using triaxial accelerometers in a random sample of Saint-Petersburg inhabitants. Material and methods: As a part of all-Russian epidemiology survey ESSE-RF there was involved random sampling of 1600 Saint-Petersburg inhabitants (25–65 years) stratified by age and sex. After that a random sub-population of 100 subjects was selected. All subjects filled in questionnaire regarding physical activity, occupation, education and nutrition. Anthropometry (weight, height with body-mass index calculation, waist circumference) was performed. Actigraph GT3X+ (Actigraph LLC, USA) accelerometer and physical activity diary were used in order to evaluate physical activity monitoring for 7 days. Adequate levels of physical activity (PA) were defined as more than 10 000 steps/day and at least 150 minutes/week of moderate and vigorous physical activity (MVPA) in bouts of 10 minutes or more. Results: 1/2 of subjects were physically active according to steps, and 1/3 according to MVPA time criteria. No gender, occupation or body composition differences were revealed in physically active and inactive subjects. Almost 50% of physically active subjects had balanced workweek-weekend PA profile, and the same criterion is true only for 13% of subjects in inactive group. In both groups the same peaks of MVPA were revealed — at 8.00–9.00 and 18.00–19.00, which are typical transportation time, but in active group these peaks were significantly higher. According to PA diaries, in most of cases physical inactivity was related to the usage of private or public transport. Conclusion: Triaxial PA-monitoring shows, that 40–60% of subjects were physically inactive, and 150-min MVPA goal can easily be achieved by only increasing walking time during transportation peaks. The physical inactivity was not determined by the type of occupation, sex or age, instead it was mainly influenced by the usage of cars in the morning and evening transportation time, rather than walking

Physical activity and risk of Amyotrophic Lateral Sclerosis in a prospective cohort study

Previous case-control studies have suggested a possible increased risk of Amyotrophic Lateral Sclerosis (ALS) with physical activity (PA), but this association has never been studied in prospective cohort studies. We therefore assessed the association between PA and risk of death from ALS in the European Prospective Investigation into Cancer and Nutrition. A total of 472,100 individuals were included in the analysis, yielding 219 ALS deaths. At recruitment, information on PA was collected thorough standardised questionnaires. Total PA was expressed by the Cambridge Physical Activity Index (CPAI) and analysed in relation to ALS mortality, using Cox hazard models. Interactions with age, sex, and anthropometric measures were assessed. Total PA was weakly inversely associated with ALS mortality with a borderline statistically significant trend across categories (p = 0.042), with those physically active being 33 % less likely to die from ALS compared to those inactive: HR = 0.67 (95 % CI 0.42-1.06). Anthropometric measures, sex, and age did not modify the association with CPAI. The present study shows a slightly decreased-not increased like in case-control studies-risk of dying from ALS in those with high levels of total PA at enrolment. This association does not appear confounded by age, gender, anthropometry, smoking, and education. Ours was the first prospective cohort study on ALS and physical activity.Peer reviewe

Life satisfaction and risk of chronic diseases in the European prospective investigation into cancer and nutrition (EPIC)-Germany study.

OBJECTIVE: The aim of the study was to examine the prospective association between life satisfaction and risk of type 2 diabetes mellitus, myocardial infarction, stroke, and cancer. Previous studies suggested that psychosocial factors may affect the development of chronic diseases but the impact of positive attitudes, in particular life satisfaction, is yet to be determined. METHODS: The analysis included 50,358 participants of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Germany study in Potsdam and Heidelberg. Life satisfaction was assessed in a baseline interview and incident cases of chronic diseases were identified and verified during follow-up. Hazard ratios were calculated using Cox proportional hazards regression models that were systematically multivariable-adjusted for established risk factors and prevalent diseases. RESULTS: During an average of 8 years of follow-up 2,293 cases of cancer, 1,840 cases of type 2 diabetes mellitus, 440 cases of stroke, and 562 cases of myocardial infarction were observed. Women who were unsatisfied with life at baseline showed in all models a significantly increased risk of cancer (HR: 1.45; 95% CI: 1.18-1.78) and stroke (HR: 1.69; 95% CI: 1.05-2.73) as well as an increased risk of type 2 diabetes mellitus by trend across categories (p-trend=0.04) compared to women very satisfied with life. In men, a relationship between life satisfaction and stroke was found but did not persist after consideration of lifestyle factors and prevalent diseases. No significant association was observed between life satisfaction and risk of myocardial infarction. CONCLUSIONS: The results of this study suggest that reduced life satisfaction is related to the development of chronic diseases--particularly in women and partly mediated by established risk factors

Sex-specific and multivariable-adjusted hazard ratios (HR) and 95%-confidence intervals (CI) of myocardial infarction incidence according to life satisfaction within EPIC Germany.

<p><b>Model 1</b>: Cox proportional hazards regression stratified by age and study center</p><p><b>Model 2</b>: model 1 with additional adjustment for smoking (never, former, current), alcohol intake (≤10 g/day, >10-40 g/day, >40 g/day), physical activity (active, moderately active, moderately inactive, inactive), education (none, primary school, technical school, secondary school, higher education/university), WHR, consumption of fruits & vegetables (g/day), red meat (g/day), and whole-grain bread (g/day)</p><p><b>Model 3</b>: model 2 with additional adjustment for prevalent hypertension and type 2 diabetes mellitus</p

Validation of anthropometric indices of adiposity against whole-body magnetic resonance imaging--a study within the German European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts.

In epidemiological studies, measures of body fat generally are obtained through anthropometric indices such as the body mass index (BMI), waist (WC), and hip circumferences (HC). Such indices, however, can only provide estimates of a person's true body fat content, overall or by adipose compartment, and may have limited accuracy, especially for the visceral adipose compartment (VAT).To determine the extent to which different body adipose tissue compartments are adequately predicted by anthropometry, and to identify anthropometric measures alone, or in combination to predict overall adiposity and specific adipose tissue compartments, independently of age and body size (height).In a sub-study of 1,192 participants of the German EPIC (European Prospective Investigation into Cancer and Nutrition) cohorts, whole-body MRI was performed to determine adipose and muscle tissue compartments. Additional anthropometric measurements of BMI, WC and HC were taken.After adjusting for age and height, BMI, WC and HC were better predictors of total body volume (TBV), total adipose tissue (TAT) and subcutaneous adipose tissue (SAT) than for VAT, coronary adipose tissue (CAT) and skeletal muscle tissue (SMT). In both sexes, BMI was the best predictor for TBV (men: r = 0.72 [0.68-0.76], women: r = 0.80 [0.77-0.83]) and SMT (men: r = 0.52 [0.45-0.57], women: r = 0.48 [0.41-0.54]). WC was the best predictor variable for TAT (r = 0.48 [0.41-0.54]), VAT (r = 0.44 [0.37-0.50]) and CAT (r = 0.34 [0.26-0.41]) (men), and for VAT (r = 0.42 [0.35-0.49]) and CAT (r = 0.29 [0.22-0.37]) (women). BMI was the best predictor for TAT (r = 0.49 [0.43-0.55]) (women). HC was the best predictor for SAT (men (r = 0.39 [0.32-0.45]) and women (r = 0.52 [0.46-0.58])).Especially the volumes of internal body fat compartments are poorly predicted by anthropometry. A possible implication may be that associations of chronic disease risks with the sizes of internal body fat as measured by BMI, WC and HC may be strongly underestimated

Prediction of body compartments by anthropometric indices in multiple linear regression analyses (Women, n = 594).

<p>Total model R² for each body compartment and partial correlation coefficients (95% CI) for anthropometric indices. All variables were adjusted for age and height. TBV = Total body volume, TAT = total adipose tissue, SAT = subcutaneous adipose tissue, VAT = visceral adipose tissue, CAT = coronary adipose tissue, SMT = skeletal muscle tissue, BMI = body mass index, WC = waist circumference, HC = hip circumference. ¹<u>Predictors included</u>: BMI, WC, HC. All variables (predictors and outcome) adjusted by age and height with the residual method. ²Partial correlation coefficients (95% CI) are reported for predictor variables.</p

Anthropometric variables and body compartments as assessed by MRI by sex and age groups¹, all values are presented as mean (min, max).

<p>TBV = total body volume, TAT = total adipose tissue, VAT = visceral adipose tissue, SAT = subcutaneous adipose tissue, CAT = coronary adipose tissue, SMT = skeletal muscle tissue.</p>1<p>Sub-study participants were sampled by baseline age groups (35–44 y, 45–54 y, 55–64 y). Due to the 4-year baseline period (1994–1998), age groups at time of sub-study (2010–2012) may overlap.</p

Pearson correlation coefficients (95% CI) between anthropometric and MRI variables adjusted for age and height with the residual method in men (n = 598).

<p>BMI = body mass index, WC = waist circumference, HC = hip circumference, TBV = total body volume, TAT = total adipose tissue, SAT = subcutaneous adipose tissue, VAT = visceral adipose tissue, CAT = coronary adipose tissue, SMT = skeletal muscle tissue.</p

a–f. Illustration of different MRI body compartments in the sub-study of the German EPIC cohorts.

<p>a–f. Illustration of different MRI body compartments in the sub-study of the German EPIC cohorts.</p

Prediction of body compartments by anthropometric indices in multiple linear regression analyses (Men, n = 598).

<p>Total model R² for each body compartment and partial correlation coefficients (95% CI) for anthropometric indices. All variables were adjusted for age and height. TBV = Total body volume, TAT = total adipose tissue, SAT = subcutaneous adipose tissue, VAT = visceral adipose tissue, CAT = coronary adipose tissue, SMT = skeletal muscle tissue, BMI = body mass index, WC = waist circumference, HC = hip circumference. ¹<u>Predictors included</u>: BMI, WC, HC. All variables (predictors and outcome) adjusted by age and height with the residual method. ²Partial correlation coefficients (95% CI) are reported for predictor variables.</p